QMUL-OpenLogo: Open Logo Detection Challenge

Description

Existing logo detection benchmarks consider
artificial deployment scenarios by assuming
that large training data with fine-grained bounding box annotations
for each class are available for model training.
Such assumptions are often invalid in realistic logo detection scenarios where
new logo classes come progressively and require to be detected
with little or none budget for exhaustively labelling fine-grained training data
for every new class.
Existing benchmarks are thus
unable to evaluate the true performance of a logo detection method
in realistic and open deployments.
In this work, we introduce a more realistic
and challenging logo detection setting, called Open Logo Detection.
Specifically,
this new setting assumes fine-grained labelling only on
a small proportion of logo classes whilst the remaining classes
have no labelled training data to simulate the
open deployment.
Further,
we create an open logo detection benchmark, called QMUL-OpenLogo,
to promote the investigation of this new challenge.
QMUL-OpenLogo contains 27,083 images from 352 logo classes,
built by aggregating and refining 7 existing datasets
and establishing an open logo detection evaluation protocol.

Statistics

For dataset training-evaluation split, we propose 3 kind of setting. The first one is the fully supervised setting,
with every logo classes contains 70% of training split and 30% of evaluation split. The second setting
split the dataset into 32 supervised classes and 320 unsupervised classes, where the supervised contains
real training split and evaluation split, while the unsupervised classes have no training split, only
evaluation split. For the 3rd setting, we split the dataset into 176 supervised classes and 176 unsupervised classes.

Notification for Supervised/Unsupervised splitting: The dataset contains images of multi-instances
and multi-classes, thus some supervised set images may contain both supervised and unsupervised instances in it,
it is recommendded to filter it for own useage.

Related Datasets

Licence

Please notice that, the QMUL-OpenLogo Dataset is made available for academic research purpose only.
All the images were collected from the existing logo detection datasets,
and the copyright belongs to the original owners.

Contact

Please feel free to send questions/comments/results(to be added in Leaderboard) to Hang Su at hang.su@qmul.ac.uk